AI is sucking up energy at an alarming rate. Gartner predicts that AI could consume up to 3.5% of global electricity by 2030. But what if quantum computing could change that? Peter Chapman of IonQ, will break down how quantum tech could reduce the power needed to fuel AI’s explosive growth and why it’s the next big thing in computing.

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Topics Covered in This Episode:
1. Quantum Computing in AI
2. Barriers to Adopting Quantum Computing
3. Mechanics of Quantum Computing
4. Quantum Computing’s Role in Energy Efficiency
5. Quantum Computing's Future Role

Timestamps:
01:45 Daily AI news
05:00 About Petter and IonQ
06:24 Quantum computers needed for complex problem solving.
09:11 Quantum cubits: electrons exist as probabilities everywhere.
12:53 Quantum computing at cusp, future applications unknown.
15:42 Quantum can address generative AI's energy demands.
18:48 Quantum power surpasses universal atoms; AI potential.
21:38 Exploring quantum processors for LLM efficiency improvement.
27:08 Reduce energy demand to address climate change.
29:20 Quantum excels in chemistry, optimization, AI tasks.
31:26 Is human intelligence inherently quantum and efficient?

Keywords:
Peter Chapman, Quantum computing, classical systems, transistors, quantum processor, AI, large language models, Prime Prompt Polished Chat GPT, efficient prompting, Quantum Processing Units, linear algebra, barriers to adoption, theoretical perceptions, cloud services, energy savings, environmental impact, nuclear power, data centers, energy demands, power plants, optimization problems, CPUs, GPUs, QPUs, drug discovery, artificial intelligence, qubits, parallelization, classical bits, 64-qubit chip.

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